Predicting Spatial Variability of Soil Organic Carbon in Delmarva Bays
| dc.contributor.author | Blumenthal, Kinsey Megan | en |
| dc.contributor.committeechair | Campbell, James B. Jr. | en |
| dc.contributor.committeemember | Juran, Luke | en |
| dc.contributor.committeemember | Lang, Megan Weiner | en |
| dc.contributor.committeemember | Galbraith, John M. | en |
| dc.contributor.department | Geography | en |
| dc.date.accessioned | 2016-12-14T09:00:42Z | en |
| dc.date.available | 2016-12-14T09:00:42Z | en |
| dc.date.issued | 2016-12-13 | en |
| dc.description.abstract | Agricultural productivity, ecosystem health, and wetland restoration rely on soil organic carbon (SOC) as vital for microbial activity and plant health. This study assessed: (1) accuracy of topographic-based non-linear models for predicting SOC; and (2) the effect of analytic strategies and soil condition on performance of spectral-based models for predicting SOC. SOC data came from 28 agriculturally converted Delmarva Bays sampled down to 1 meter. R2 was used as an indicator of model performance. For topographic-based modeling, correlation coefficients and condition indices reduced 50 terrain-related values to three datasets of 16, 11, and 7 variables. Five types of non-linear models were examined: Generalized Linear Mondel (GLM) ridge, GLM LASSO, Generalized Additive Model (GAM) non-penalized, GAM cubic splice, and partial least-squares regression. Carbon stocks varied widely, 50 to 219 Mg/ha, with the average around 93 Mg/ha. Topography shared a weak relationship to SOC with most attributes showing a correlation coefficient less than 0.3. GLM ridge and both GAMs achieved moderate accuracy at least once, usually using the 16 or 11 variable datasets. GAMs consistently performed the best. Prior to carbon analysis, hyperspectral signatures were recorded for the topmost soil horizons under different conditions: moist unground, dry unground, and dry ground. Twenty-four math treatment and smoothing technique combinations were run on each hyperspectral dataset. R2 varied greatly within datasets depending on analytic strategy, but all datasets returned an R2 greater than 0.9 at least twice. Moist unground soil models outperformed the others when comparing the best models among datasets. | en |
| dc.description.abstractgeneral | Delmarva Bays are depressional landforms found throughout the Delmarva Peninsula that provide habitat for a number of endangered amphibian and plant species. Due to the prevalence of these Bays on the peninsula and their location in a highly agriculturalized landscape, many Delmarva Bays have been converted from wetlands into farmland. Whether a Bay is a wetland or agricultural land, organic carbon is an important soil property for a large number of microorganisms and plant health. Increased levels of soil organic carbon (SOC) have been linked with more diversity in soil biota and increased nutrient availability, which affect cropland productivity and ecosystem health. SOC stock and distribution is useful information to help formulate land management practices. However, SOC varies horizontally across a landscape and traditional methods for gathering data are time intensive. This study looked at the potential accuracy of two types of models for predicting SOC variation in agriculturally converted Delmarva Bays: 1) models based on terrain-related attributes, and 2) models based on soil spectral data. Using data collected from 28 agriculturally converted Bays, moderate to high potential accuracy was returned for both types of models. Results suggest terrain-related and spectral-based models may be useful alternatives to traditional soil sampling for looking at SOC variation to inform land management decisions regarding these Bays. | en |
| dc.description.degree | Master of Science | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:9408 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/73692 | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | Carbon | en |
| dc.subject | Non-linear Modeling | en |
| dc.subject | Topography | en |
| dc.subject | Hyperspectral | en |
| dc.title | Predicting Spatial Variability of Soil Organic Carbon in Delmarva Bays | en |
| dc.type | Thesis | en |
| thesis.degree.discipline | Geography | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | masters | en |
| thesis.degree.name | Master of Science | en |
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